Senior Manager, Data Sciences

Bristol Myers Squibb; Uxbridge; On-site; GBP 90,000 - 120,000; Full time

Job summary

Bristol Myers Squibb in the United Kingdom is seeking a Senior Manager, Data Science to lead cutting-edge analytics across translational to late-stage drug development. You will build reproducible, scalable pipelines and apply AI/ML methods to generate actionable insights for clinical decisions.

The role requires deep experience with clinical trial data or EHRs, strong programming in Python and R, and collaboration with clinicians, biostatisticians, and data engineers to drive methodological

Qualifications

• PhD in a quantitative field with 1+ years of academia/industry experience; or Master's with 3+ years in industry.
• Strong experience in data science and statistical analysis using clinical trial or EHR data in pharma R&D.
• Experience building and validating ML models on high-dimensional data for time-to-event and longitudinal outcomes.
• Proficiency in Python, R, SQL and cloud platforms (AWS, Azure, Databricks).
• Understanding of clinical trial design and drug development processes.

Responsibilities

• Develop computational methods for patient segmentation, biomarker discovery, and hypothesis generation from multimodal datasets.
• Execute analyses on datasets from clinical trials and real-world cohorts (omics, imaging, flow cytometry).
• Develop integration and visualization techniques for high-dimensional data across early-to-late phase programs.
• Formulate and validate predictive models; establish scalable, automated modelling pipelines.
• Apply AI/ML, NLP, causal ML, and explainable AI across data modalities and contexts.
• Collaborate with statisticians and cross-functional teams to influence study design and decision making.

Skills

Data science

Statistics

Time-to-event analysis

Longitudinal data

Cross-functional collaboration

Education

PhD in Computational Biology/ Biostatistics/ Statistics/ Biomedical Engineering/ CS

Master's in a quantitative field

Tools

Python

R

SQL

AWS/Azure/Databricks

Job description

At Bristol Myers Squibb, our employees often ask, "Who are you working for?"-a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Drive Insight at the Cutting Edge of Drug Development

Are you a hands-on data scientist with a passion for turning complex, multi-modal data into actionable insights that shape clinical decisions? Bristol Myers Squibb is seeking a Senior Manager, Data Science to join our Drug Development Data Science & Advanced Analytics (DSAA) team.

What You'll Do

Data Science & Analytics

• Develop and apply novel computational methods for patient segmentation, biomarker discovery, and hypothesis generation from multimodal clinical and omics datasets, in partnership with Translational, Clinical, and Statistical Scientists
• Execute data science analyses on datasets from BMS clinical trials and real-world data cohorts, spanning genomics, proteomics, imaging, flow cytometry, and other high-dimensional biomarker data types
• Develop innovative approaches to integrating, mining, and visualising diverse, high-dimensional, and disparate datasets generated across early-to-late phase drug development
• Formulate, implement, test, and validate predictive models and build efficient, automated processes for delivering modelling results at scale
• Apply modern machine learning capabilities - including AI/ML, deep learning, NLP, causal ML, and explainable AI - across multiple data modalities and clinical development contexts
• Apply statistically rigorous approaches to clinical trial data, including survival analysis, longitudinal/mixed-effects modelling, and appropriate handling of missing data and censoring
• Contribute to the scientific and statistical strategy of drug development programs, including the development of predictive biomarkers, novel trial designs, and precision medicine approaches

Data Engineering & Reproducibility

• Build and maintain well-structured, reproducible, version-controlled analytical pipelines and codebases using Python, R, SQL, and cloud platforms
• Develop and apply data quality frameworks to assess and ensure fitness-for-purpose of diverse data sources for specific analytical questions
• Implement strong model evaluation practices including cross-validation strategies, calibration assessment, and transparent reporting of model performance and limitations
• Build scalable, automated processes for delivering analytical results across multiple programs and data types

Collaboration & Technical Contribution

• Partner with lead and protocol statisticians in contributing to statistical analysis plans (SAPs) for exploratory data science analyses supporting drug development programs
• Collaborate with cross-functional teams including clinicians, translational medicine scientists, biostatisticians, data engineers, and IT/engineering professionals
• Contribute to team excellence through code reviews, technical mentorship, and raising the overall engineering and methodological standards of the team
• Communicate analytical strategies and results clearly and effectively to both technical and non-technical stakeholders, with strong data presentation and visualisation skills
• Manage and coordinate deliverables across concurrent, fast-paced projects within tight timelines

What We're Looking For

Required Qualifications:

• PhD in a relevant quantitative field (e.g., Computational Biology, Biostatistics, Statistics, Biomedical Engineering, or Computer Science) with 1+ years of academic/industry experience; or a Master's Degree in a relevant quantitative field with 3+ years of industry experience
• Strong experience in data science and statistical analysis using clinical trial or electronic health records data, particularly in a pharma R&D context
• Experience developing and validating statistical and machine learning models on high-dimensional data for time-to-event, longitudinal, and multivariate outcomes
• Experience in the application of AI/ML and proficiency in Python, R, SQL , and cloud platforms (e.g., AWS, Azure, Databricks)
• Familiarity with clinical trial design, drug development processes, and the role of biomarkers in regulatory and clinical decision-making
• A perspective on leveraging innovati